The chest x-ray is useful for detecting patient conditions and for imaging a range of skeletal and organ structures. Radiographic images of the chest are useful for detection of lung nodules and other features that indicate lung cancer and other pathologic structures and life-threatening conditions. In clinical applications such as in the Intensive Care Unit (ICU), chest x-rays can have particular value for indicating pneumothorax as well as for tube/line positioning, and other clinical conditions. To view the lung fields more clearly and allow more accurate analysis of a patient's condition, it is useful to suppress the rib cage and related features in the chest x-ray, without losing detail of the lung tissue or other features within the chest cavity.
Methods have been proposed for detecting and suppressing rib structures and allowing the radiologist to view the lung fields without perceptible obstruction by the ribs. Some methods have used template matching, rib edge detection, or curve fitting edge detection. The applicants have recognized that it can be challenging to remove rib features from the chest x-ray image without degrading the underlying image content that can include lung tissue.
U.S. Pat. No. 8,204,292 entitled “Feature-based neural network regression for feature suppression” (Knapp) describes the use of a trained system for predicting rib components and subsequently subtracting the predicted rib components.
US Patent Application Publication No. 2009/0060366 entitled “Object segmentation in images” (Worrell) describes alternative techniques using detected rib edges to identify rib structures.
The article entitled “Image-Processing Technique for Suppressing Ribs in Chest Radiographs by Means of Massive Training Artificial Neural Network (MTANN)” by Suzuki et al. in IEEE Transactions on Medical Imaging, Vol. 25 No. 4, April 2006 describes methods for detection of lung nodules and other features using learned results from a database to optimize rib suppression for individual patient images.
The article entitled “Detection and Compensation of Rib Structures in Chest Radiographs for Diagnose Assistance” in Proceedings of SPIE, 3338:774-785 (1998) by Vogelsang et al. describes methods for compensating for rib structures in a radiographic image. Among techniques described in the Vogelsang et al. article are template matching and generation and selection from candidate parabolas for tracing rib edges.
The article entitled “Model based analysis of chest radiographs”, in Proceedings of SPIE 3979, 1040 (2000), also by Vogelsang et al. describes Bezier curve matching to find rib edges in a chest radiograph for alignment of a model and subsequent rib shadow compensation.
While some of these methods may have achieved a level of success using rib edge detection to identify rib structures that can then be suppressed in the x-ray image, improvements are desired.
Thus, there is a need for a method of rib suppression that accurately detects ribs in chest x-ray images and suppresses the rib area in a chest x-ray image, while preserving the image content of underlying lung tissue.